Upload agent

agent.json

@@ -1,7 +1,7 @@
 {
-    "class": "ToolCallingAgent",
+    "class": "CodeAgent",
     "tools": [
-        "duckduckgo_search_and_visit",
+        "get_today_datetime",
         "final_answer"
     ],
     "model": {
@@ -13,11 +13,11 @@
     },
     "managed_agents": {},
     "prompt_templates": {
-        "system_prompt": "You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.\nTo do so, you have been given access to some tools.\n\nThe tool call you write is an action: after the tool is executed, you will get the result of the tool call as an \"observation\".\nThis Action/Observation can repeat N times, you should take several steps when needed.\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like \"image_1.jpg\".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: \"image_1.jpg\"\n\nAction:\n{\n  \"name\": \"image_transformer\",\n  \"arguments\": {\"image\": \"image_1.jpg\"}\n}\n\nTo provide the final answer to the task, use an action blob with \"name\": \"final_answer\" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n  \"name\": \"final_answer\",\n  \"arguments\": {\"answer\": \"insert your final answer here\"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nAction:\n{\n  \"name\": \"document_qa\",\n  \"arguments\": {\"document\": \"document.pdf\", \"question\": \"Who is the oldest person mentioned?\"}\n}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nAction:\n{\n  \"name\": \"image_generator\",\n  \"arguments\": {\"prompt\": \"A portrait of John Doe, a 55-year-old man living in Canada.\"}\n}\nObservation: \"image.png\"\n\nAction:\n{\n  \"name\": \"final_answer\",\n  \"arguments\": \"image.png\"\n}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nAction:\n{\n    \"name\": \"python_interpreter\",\n    \"arguments\": {\"code\": \"5 + 3 + 1294.678\"}\n}\nObservation: 1302.678\n\nAction:\n{\n  \"name\": \"final_answer\",\n  \"arguments\": \"1302.678\"\n}\n\n---\nTask: \"Which city has the highest population , Guangzhou or Shanghai?\"\n\nAction:\n{\n    \"name\": \"web_search\",\n    \"arguments\": \"Population Guangzhou\"\n}\nObservation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\n\n\nAction:\n{\n    \"name\": \"web_search\",\n    \"arguments\": \"Population Shanghai\"\n}\nObservation: '26 million (2019)'\n\nAction:\n{\n  \"name\": \"final_answer\",\n  \"arguments\": \"Shanghai\"\n}\n\nAbove example were using notional tools that might not exist for you. You only have access to these tools:\n{%- for tool in tools.values() %}\n- {{ tool.to_tool_calling_prompt() }}\n{%- endfor %}\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n{%- for agent in managed_agents.values() %}\n- {{ agent.name }}: {{ agent.description }}\n  - Takes inputs: {{agent.inputs}}\n  - Returns an output of type: {{agent.output_type}}\n{%- endfor %}\n{%- endif %}\n\n{%- if custom_instructions %}\n{{custom_instructions}}\n{%- endif %}\n\nHere are the rules you should always follow to solve your task:\n1. ALWAYS provide a tool call, else you will fail.\n2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.\n3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.\n4. Never re-do a tool call that you previously did with the exact same parameters.\n\nNow Begin!",
+        "system_prompt": "You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.\nTo do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.\n\nAt each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.\nThen in the Code sequence you should write the code in simple Python. The code sequence must be opened with '{{code_block_opening_tag}}', and closed with '{{code_block_closing_tag}}'.\nDuring each intermediate step, you can use 'print()' to save whatever important information you will then need.\nThese print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: \"Generate an image of the oldest person in this document.\"\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\n{{code_block_opening_tag}}\nanswer = document_qa(document=document, question=\"Who is the oldest person mentioned?\")\nprint(answer)\n{{code_block_closing_tag}}\nObservation: \"The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland.\"\n\nThought: I will now generate an image showcasing the oldest person.\n{{code_block_opening_tag}}\nimage = image_generator(\"A portrait of John Doe, a 55-year-old man living in Canada.\")\nfinal_answer(image)\n{{code_block_closing_tag}}\n\n---\nTask: \"What is the result of the following operation: 5 + 3 + 1294.678?\"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\n{{code_block_opening_tag}}\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n{{code_block_closing_tag}}\n\n---\nTask:\n\"Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.\nYou have been provided with these additional arguments, that you can access using the keys as variables in your python code:\n{'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}\"\n\nThought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\n{{code_block_opening_tag}}\ntranslated_question = translator(question=question, src_lang=\"French\", tgt_lang=\"English\")\nprint(f\"The translated question is {translated_question}.\")\nanswer = image_qa(image=image, question=translated_question)\nfinal_answer(f\"The answer is {answer}\")\n{{code_block_closing_tag}}\n\n---\nTask:\nIn a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.\nWhat does he say was the consequence of Einstein learning too much math on his creativity, in one word?\n\nThought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.\n{{code_block_opening_tag}}\npages = web_search(query=\"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\")\nprint(pages)\n{{code_block_closing_tag}}\nObservation:\nNo result found for query \"1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\".\n\nThought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.\n{{code_block_opening_tag}}\npages = web_search(query=\"1979 interview Stanislaus Ulam\")\nprint(pages)\n{{code_block_closing_tag}}\nObservation:\nFound 6 pages:\n[Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)\n\n[Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)\n\n(truncated)\n\nThought: I will read the first 2 pages to know more.\n{{code_block_opening_tag}}\nfor url in [\"https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/\", \"https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/\"]:\n    whole_page = visit_webpage(url)\n    print(whole_page)\n    print(\"\\n\" + \"=\"*80 + \"\\n\")  # Print separator between pages\n{{code_block_closing_tag}}\nObservation:\nManhattan Project Locations:\nLos Alamos, NM\nStanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at\n(truncated)\n\nThought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: \"He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity.\" Let's answer in one word.\n{{code_block_opening_tag}}\nfinal_answer(\"diminished\")\n{{code_block_closing_tag}}\n\n---\nTask: \"Which city has the highest population: Guangzhou or Shanghai?\"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.\n{{code_block_opening_tag}}\nfor city in [\"Guangzhou\", \"Shanghai\"]:\n    print(f\"Population {city}:\", web_search(f\"{city} population\")\n{{code_block_closing_tag}}\nObservation:\nPopulation Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']\nPopulation Shanghai: '26 million (2019)'\n\nThought: Now I know that Shanghai has the highest population.\n{{code_block_opening_tag}}\nfinal_answer(\"Shanghai\")\n{{code_block_closing_tag}}\n\n---\nTask: \"What is the current age of the pope, raised to the power 0.36?\"\n\nThought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.\n{{code_block_opening_tag}}\npope_age_wiki = wikipedia_search(query=\"current pope age\")\nprint(\"Pope age as per wikipedia:\", pope_age_wiki)\npope_age_search = web_search(query=\"current pope age\")\nprint(\"Pope age as per google search:\", pope_age_search)\n{{code_block_closing_tag}}\nObservation:\nPope age: \"The pope Francis is currently 88 years old.\"\n\nThought: I know that the pope is 88 years old. Let's compute the result using python code.\n{{code_block_opening_tag}}\npope_current_age = 88 ** 0.36\nfinal_answer(pope_current_age)\n{{code_block_closing_tag}}\n\nAbove example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:\n{{code_block_opening_tag}}\n{%- for tool in tools.values() %}\n{{ tool.to_code_prompt() }}\n{% endfor %}\n{{code_block_closing_tag}}\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n{{code_block_opening_tag}}\n{%- for agent in managed_agents.values() %}\ndef {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:\n    \"\"\"{{ agent.description }}\n\n    Args:\n        task: Long detailed description of the task.\n        additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.\n    \"\"\"\n{% endfor %}\n{{code_block_closing_tag}}\n{%- endif %}\n\nHere are the rules you should always follow to solve your task:\n1. Always provide a 'Thought:' sequence, and a '{{code_block_opening_tag}}' sequence ending with '{{code_block_closing_tag}}', else you will fail.\n2. Use only variables that you have defined!\n3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wikipedia_search({'query': \"What is the place where James Bond lives?\"})', but use the arguments directly as in 'answer = wikipedia_search(query=\"What is the place where James Bond lives?\")'.\n4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to wikipedia_search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.\n5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.\n6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.\n7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.\n8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}\n9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.\n10. Don't give up! You're in charge of solving the task, not providing directions to solve it.\n\n{%- if custom_instructions %}\n{{custom_instructions}}\n{%- endif %}\n\nNow Begin!",
         "planning": {
-            "initial_plan": "You are a world expert at analyzing a situation to derive facts, and plan accordingly towards solving a task.\nBelow I will present you a task. You will need to 1. build a survey of facts known or needed to solve the task, then 2. make a plan of action to solve the task.\n\n## 1. Facts survey\nYou will build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.\nThese \"facts\" will typically be specific names, dates, values, etc. Your answer should use the below headings:\n### 1.1. Facts given in the task\nList here the specific facts given in the task that could help you (there might be nothing here).\n\n### 1.2. Facts to look up\nList here any facts that we may need to look up.\nAlso list where to find each of these, for instance a website, a file... - maybe the task contains some sources that you should re-use here.\n\n### 1.3. Facts to derive\nList here anything that we want to derive from the above by logical reasoning, for instance computation or simulation.\n\nDon't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.\n\n## 2. Plan\nThen for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.\nThis plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.\nDo not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.\nAfter writing the final step of the plan, write the '<end_plan>' tag and stop there.\n\nYou can leverage these tools:\n{%- for tool in tools.values() %}\n- {{ tool.to_tool_calling_prompt() }}\n{%- endfor %}\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n{%- for agent in managed_agents.values() %}\n- {{ agent.name }}: {{ agent.description }}\n  - Takes inputs: {{agent.inputs}}\n  - Returns an output of type: {{agent.output_type}}\n{%- endfor %}\n{%- endif %}\n\n---\nNow begin! Here is your task:\n```\n{{task}}\n```\nFirst in part 1, write the facts survey, then in part 2, write your plan.",
+            "initial_plan": "You are a world expert at analyzing a situation to derive facts, and plan accordingly towards solving a task.\nBelow I will present you a task. You will need to 1. build a survey of facts known or needed to solve the task, then 2. make a plan of action to solve the task.\n\n## 1. Facts survey\nYou will build a comprehensive preparatory survey of which facts we have at our disposal and which ones we still need.\nThese \"facts\" will typically be specific names, dates, values, etc. Your answer should use the below headings:\n### 1.1. Facts given in the task\nList here the specific facts given in the task that could help you (there might be nothing here).\n\n### 1.2. Facts to look up\nList here any facts that we may need to look up.\nAlso list where to find each of these, for instance a website, a file... - maybe the task contains some sources that you should re-use here.\n\n### 1.3. Facts to derive\nList here anything that we want to derive from the above by logical reasoning, for instance computation or simulation.\n\nDon't make any assumptions. For each item, provide a thorough reasoning. Do not add anything else on top of three headings above.\n\n## 2. Plan\nThen for the given task, develop a step-by-step high-level plan taking into account the above inputs and list of facts.\nThis plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.\nDo not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.\nAfter writing the final step of the plan, write the '<end_plan>' tag and stop there.\n\nYou can leverage these tools, behaving like regular python functions:\n```python\n{%- for tool in tools.values() %}\n{{ tool.to_code_prompt() }}\n{% endfor %}\n```\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n```python\n{%- for agent in managed_agents.values() %}\ndef {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:\n    \"\"\"{{ agent.description }}\n\n    Args:\n        task: Long detailed description of the task.\n        additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.\n    \"\"\"\n{% endfor %}\n```\n{%- endif %}\n\n---\nNow begin! Here is your task:\n```\n{{task}}\n```\nFirst in part 1, write the facts survey, then in part 2, write your plan.",
             "update_plan_pre_messages": "You are a world expert at analyzing a situation, and plan accordingly towards solving a task.\nYou have been given the following task:\n```\n{{task}}\n```\n\nBelow you will find a history of attempts made to solve this task.\nYou will first have to produce a survey of known and unknown facts, then propose a step-by-step high-level plan to solve the task.\nIf the previous tries so far have met some success, your updated plan can build on these results.\nIf you are stalled, you can make a completely new plan starting from scratch.\n\nFind the task and history below:",
-            "update_plan_post_messages": "Now write your updated facts below, taking into account the above history:\n## 1. Updated facts survey\n### 1.1. Facts given in the task\n### 1.2. Facts that we have learned\n### 1.3. Facts still to look up\n### 1.4. Facts still to derive\n\nThen write a step-by-step high-level plan to solve the task above.\n## 2. Plan\n### 2. 1. ...\nEtc.\nThis plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.\nBeware that you have {remaining_steps} steps remaining.\nDo not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.\nAfter writing the final step of the plan, write the '<end_plan>' tag and stop there.\n\nYou can leverage these tools:\n{%- for tool in tools.values() %}\n- {{ tool.to_tool_calling_prompt() }}\n{%- endfor %}\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n{%- for agent in managed_agents.values() %}\n- {{ agent.name }}: {{ agent.description }}\n  - Takes inputs: {{agent.inputs}}\n  - Returns an output of type: {{agent.output_type}}\n{%- endfor %}\n{%- endif %}\n\nNow write your new plan below."
+            "update_plan_post_messages": "Now write your updated facts below, taking into account the above history:\n## 1. Updated facts survey\n### 1.1. Facts given in the task\n### 1.2. Facts that we have learned\n### 1.3. Facts still to look up\n### 1.4. Facts still to derive\n\nThen write a step-by-step high-level plan to solve the task above.\n## 2. Plan\n### 2. 1. ...\nEtc.\nThis plan should involve individual tasks based on the available tools, that if executed correctly will yield the correct answer.\nBeware that you have {remaining_steps} steps remaining.\nDo not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.\nAfter writing the final step of the plan, write the '<end_plan>' tag and stop there.\n\nYou can leverage these tools, behaving like regular python functions:\n```python\n{%- for tool in tools.values() %}\n{{ tool.to_code_prompt() }}\n{% endfor %}\n```\n\n{%- if managed_agents and managed_agents.values() | list %}\nYou can also give tasks to team members.\nCalling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.\nYou can also include any relevant variables or context using the 'additional_args' argument.\nHere is a list of the team members that you can call:\n```python\n{%- for agent in managed_agents.values() %}\ndef {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:\n    \"\"\"{{ agent.description }}\n\n    Args:\n        task: Long detailed description of the task.\n        additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.\n    \"\"\"\n{% endfor %}\n```\n{%- endif %}\n\nNow write your updated facts survey below, then your new plan."
         },
         "managed_agent": {
             "task": "You're a helpful agent named '{{name}}'.\nYou have been submitted this task by your manager.\n---\nTask:\n{{task}}\n---\nYou're helping your manager solve a wider task: so make sure to not provide a one-line answer, but give as much information as possible to give them a clear understanding of the answer.\n\nYour final_answer WILL HAVE to contain these parts:\n### 1. Task outcome (short version):\n### 2. Task outcome (extremely detailed version):\n### 3. Additional context (if relevant):\n\nPut all these in your final_answer tool, everything that you do not pass as an argument to final_answer will be lost.\nAnd even if your task resolution is not successful, please return as much context as possible, so that your manager can act upon this feedback.",
@@ -34,8 +34,22 @@
     "name": null,
     "description": null,
     "requirements": [
-        "ddgs",
-        "newspaper",
         "smolagents"
-    ]
+    ],
+    "authorized_imports": [
+        "collections",
+        "datetime",
+        "itertools",
+        "math",
+        "queue",
+        "random",
+        "re",
+        "stat",
+        "statistics",
+        "time",
+        "unicodedata"
+    ],
+    "executor_type": "local",
+    "executor_kwargs": {},
+    "max_print_outputs_length": null
 }

app.py

@@ -1,11 +1,11 @@
 import yaml
 import os
-from smolagents import GradioUI, ToolCallingAgent, LiteLLMModel
+from smolagents import GradioUI, CodeAgent, LiteLLMModel
 
 # Get current directory path
 CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
 
-from tools.duckduckgo_search_and_visit import DuckDuckGoSearchAndVisit as DuckduckgoSearchAndVisit
+from tools.get_today_datetime import SimpleTool as GetTodayDatetime
 from tools.final_answer import FinalAnswerTool as FinalAnswer
 
 
@@ -15,22 +15,25 @@ model_id='gemini/gemini-2.5-flash',
 api_base=None,
 )
 
-duckduckgo_search_and_visit = DuckduckgoSearchAndVisit()
+get_today_datetime = GetTodayDatetime()
 final_answer = FinalAnswer()
 
 
 with open(os.path.join(CURRENT_DIR, "prompts.yaml"), 'r') as stream:
     prompt_templates = yaml.safe_load(stream)
 
-agent = ToolCallingAgent(
+agent = CodeAgent(
     model=model,
-    tools=[duckduckgo_search_and_visit],
+    tools=[get_today_datetime],
     managed_agents=[],
     max_steps=20,
     verbosity_level=1,
     planning_interval=None,
     name=None,
     description=None,
+    executor_type='local',
+    executor_kwargs={},
+    max_print_outputs_length=None,
     prompt_templates=prompt_templates
 )
 if __name__ == "__main__":

prompts.yaml

@@ -1,121 +1,175 @@
 "system_prompt": |-
-  You are an expert assistant who can solve any task using tool calls. You will be given a task to solve as best you can.
-  To do so, you have been given access to some tools.
-
-  The tool call you write is an action: after the tool is executed, you will get the result of the tool call as an "observation".
-  This Action/Observation can repeat N times, you should take several steps when needed.
-
-  You can use the result of the previous action as input for the next action.
-  The observation will always be a string: it can represent a file, like "image_1.jpg".
-  Then you can use it as input for the next action. You can do it for instance as follows:
-
-  Observation: "image_1.jpg"
-
-  Action:
-  {
-    "name": "image_transformer",
-    "arguments": {"image": "image_1.jpg"}
-  }
-
-  To provide the final answer to the task, use an action blob with "name": "final_answer" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:
-  Action:
-  {
-    "name": "final_answer",
-    "arguments": {"answer": "insert your final answer here"}
-  }
+  You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
+  To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
+  To solve the task, you must plan forward to proceed in a series of steps, in a cycle of Thought, Code, and Observation sequences.
 
+  At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
+  Then in the Code sequence you should write the code in simple Python. The code sequence must be opened with '{{code_block_opening_tag}}', and closed with '{{code_block_closing_tag}}'.
+  During each intermediate step, you can use 'print()' to save whatever important information you will then need.
+  These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
+  In the end you have to return a final answer using the `final_answer` tool.
 
   Here are a few examples using notional tools:
   ---
   Task: "Generate an image of the oldest person in this document."
 
-  Action:
-  {
-    "name": "document_qa",
-    "arguments": {"document": "document.pdf", "question": "Who is the oldest person mentioned?"}
-  }
+  Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
+  {{code_block_opening_tag}}
+  answer = document_qa(document=document, question="Who is the oldest person mentioned?")
+  print(answer)
+  {{code_block_closing_tag}}
   Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
 
-  Action:
-  {
-    "name": "image_generator",
-    "arguments": {"prompt": "A portrait of John Doe, a 55-year-old man living in Canada."}
-  }
-  Observation: "image.png"
-
-  Action:
-  {
-    "name": "final_answer",
-    "arguments": "image.png"
-  }
+  Thought: I will now generate an image showcasing the oldest person.
+  {{code_block_opening_tag}}
+  image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
+  final_answer(image)
+  {{code_block_closing_tag}}
 
   ---
   Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
 
-  Action:
-  {
-      "name": "python_interpreter",
-      "arguments": {"code": "5 + 3 + 1294.678"}
-  }
-  Observation: 1302.678
+  Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
+  {{code_block_opening_tag}}
+  result = 5 + 3 + 1294.678
+  final_answer(result)
+  {{code_block_closing_tag}}
 
-  Action:
-  {
-    "name": "final_answer",
-    "arguments": "1302.678"
-  }
+  ---
+  Task:
+  "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
+  You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
+  {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
+
+  Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
+  {{code_block_opening_tag}}
+  translated_question = translator(question=question, src_lang="French", tgt_lang="English")
+  print(f"The translated question is {translated_question}.")
+  answer = image_qa(image=image, question=translated_question)
+  final_answer(f"The answer is {answer}")
+  {{code_block_closing_tag}}
 
   ---
-  Task: "Which city has the highest population , Guangzhou or Shanghai?"
-
-  Action:
-  {
-      "name": "web_search",
-      "arguments": "Population Guangzhou"
-  }
-  Observation: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
-
-
-  Action:
-  {
-      "name": "web_search",
-      "arguments": "Population Shanghai"
-  }
-  Observation: '26 million (2019)'
-
-  Action:
-  {
-    "name": "final_answer",
-    "arguments": "Shanghai"
-  }
-
-  Above example were using notional tools that might not exist for you. You only have access to these tools:
+  Task:
+  In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
+  What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
+
+  Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
+  {{code_block_opening_tag}}
+  pages = web_search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
+  print(pages)
+  {{code_block_closing_tag}}
+  Observation:
+  No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
+
+  Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
+  {{code_block_opening_tag}}
+  pages = web_search(query="1979 interview Stanislaus Ulam")
+  print(pages)
+  {{code_block_closing_tag}}
+  Observation:
+  Found 6 pages:
+  [Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
+
+  [Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
+
+  (truncated)
+
+  Thought: I will read the first 2 pages to know more.
+  {{code_block_opening_tag}}
+  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
+      whole_page = visit_webpage(url)
+      print(whole_page)
+      print("\n" + "="*80 + "\n")  # Print separator between pages
+  {{code_block_closing_tag}}
+  Observation:
+  Manhattan Project Locations:
+  Los Alamos, NM
+  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
+  (truncated)
+
+  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
+  {{code_block_opening_tag}}
+  final_answer("diminished")
+  {{code_block_closing_tag}}
+
+  ---
+  Task: "Which city has the highest population: Guangzhou or Shanghai?"
+
+  Thought: I need to get the populations for both cities and compare them: I will use the tool `web_search` to get the population of both cities.
+  {{code_block_opening_tag}}
+  for city in ["Guangzhou", "Shanghai"]:
+      print(f"Population {city}:", web_search(f"{city} population")
+  {{code_block_closing_tag}}
+  Observation:
+  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
+  Population Shanghai: '26 million (2019)'
+
+  Thought: Now I know that Shanghai has the highest population.
+  {{code_block_opening_tag}}
+  final_answer("Shanghai")
+  {{code_block_closing_tag}}
+
+  ---
+  Task: "What is the current age of the pope, raised to the power 0.36?"
+
+  Thought: I will use the tool `wikipedia_search` to get the age of the pope, and confirm that with a web search.
+  {{code_block_opening_tag}}
+  pope_age_wiki = wikipedia_search(query="current pope age")
+  print("Pope age as per wikipedia:", pope_age_wiki)
+  pope_age_search = web_search(query="current pope age")
+  print("Pope age as per google search:", pope_age_search)
+  {{code_block_closing_tag}}
+  Observation:
+  Pope age: "The pope Francis is currently 88 years old."
+
+  Thought: I know that the pope is 88 years old. Let's compute the result using python code.
+  {{code_block_opening_tag}}
+  pope_current_age = 88 ** 0.36
+  final_answer(pope_current_age)
+  {{code_block_closing_tag}}
+
+  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools, behaving like regular python functions:
+  {{code_block_opening_tag}}
   {%- for tool in tools.values() %}
-  - {{ tool.to_tool_calling_prompt() }}
-  {%- endfor %}
+  {{ tool.to_code_prompt() }}
+  {% endfor %}
+  {{code_block_closing_tag}}
 
   {%- if managed_agents and managed_agents.values() | list %}
   You can also give tasks to team members.
   Calling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.
   You can also include any relevant variables or context using the 'additional_args' argument.
   Here is a list of the team members that you can call:
+  {{code_block_opening_tag}}
   {%- for agent in managed_agents.values() %}
-  - {{ agent.name }}: {{ agent.description }}
-    - Takes inputs: {{agent.inputs}}
-    - Returns an output of type: {{agent.output_type}}
-  {%- endfor %}
+  def {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:
+      """{{ agent.description }}
+
+      Args:
+          task: Long detailed description of the task.
+          additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.
+      """
+  {% endfor %}
+  {{code_block_closing_tag}}
   {%- endif %}
 
+  Here are the rules you should always follow to solve your task:
+  1. Always provide a 'Thought:' sequence, and a '{{code_block_opening_tag}}' sequence ending with '{{code_block_closing_tag}}', else you will fail.
+  2. Use only variables that you have defined!
+  3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wikipedia_search({'query': "What is the place where James Bond lives?"})', but use the arguments directly as in 'answer = wikipedia_search(query="What is the place where James Bond lives?")'.
+  4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to wikipedia_search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.
+  5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
+  6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
+  7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
+  8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
+  9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
+  10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
+
   {%- if custom_instructions %}
   {{custom_instructions}}
   {%- endif %}
 
-  Here are the rules you should always follow to solve your task:
-  1. ALWAYS provide a tool call, else you will fail.
-  2. Always use the right arguments for the tools. Never use variable names as the action arguments, use the value instead.
-  3. Call a tool only when needed: do not call the search agent if you do not need information, try to solve the task yourself. If no tool call is needed, use final_answer tool to return your answer.
-  4. Never re-do a tool call that you previously did with the exact same parameters.
-
   Now Begin!
 "planning":
   "initial_plan": |-
@@ -143,21 +197,29 @@
     Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
     After writing the final step of the plan, write the '<end_plan>' tag and stop there.
 
-    You can leverage these tools:
+    You can leverage these tools, behaving like regular python functions:
+    ```python
     {%- for tool in tools.values() %}
-    - {{ tool.to_tool_calling_prompt() }}
-    {%- endfor %}
+    {{ tool.to_code_prompt() }}
+    {% endfor %}
+    ```
 
     {%- if managed_agents and managed_agents.values() | list %}
     You can also give tasks to team members.
     Calling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.
     You can also include any relevant variables or context using the 'additional_args' argument.
     Here is a list of the team members that you can call:
+    ```python
     {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-      - Takes inputs: {{agent.inputs}}
-      - Returns an output of type: {{agent.output_type}}
-    {%- endfor %}
+    def {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:
+        """{{ agent.description }}
+
+        Args:
+            task: Long detailed description of the task.
+            additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.
+        """
+    {% endfor %}
+    ```
     {%- endif %}
 
     ---
@@ -196,24 +258,32 @@
     Do not skip steps, do not add any superfluous steps. Only write the high-level plan, DO NOT DETAIL INDIVIDUAL TOOL CALLS.
     After writing the final step of the plan, write the '<end_plan>' tag and stop there.
 
-    You can leverage these tools:
+    You can leverage these tools, behaving like regular python functions:
+    ```python
     {%- for tool in tools.values() %}
-    - {{ tool.to_tool_calling_prompt() }}
-    {%- endfor %}
+    {{ tool.to_code_prompt() }}
+    {% endfor %}
+    ```
 
     {%- if managed_agents and managed_agents.values() | list %}
     You can also give tasks to team members.
     Calling a team member works similarly to calling a tool: provide the task description as the 'task' argument. Since this team member is a real human, be as detailed and verbose as necessary in your task description.
     You can also include any relevant variables or context using the 'additional_args' argument.
     Here is a list of the team members that you can call:
+    ```python
     {%- for agent in managed_agents.values() %}
-    - {{ agent.name }}: {{ agent.description }}
-      - Takes inputs: {{agent.inputs}}
-      - Returns an output of type: {{agent.output_type}}
-    {%- endfor %}
+    def {{ agent.name }}(task: str, additional_args: dict[str, Any]) -> str:
+        """{{ agent.description }}
+
+        Args:
+            task: Long detailed description of the task.
+            additional_args: Dictionary of extra inputs to pass to the managed agent, e.g. images, dataframes, or any other contextual data it may need.
+        """
+    {% endfor %}
+    ```
     {%- endif %}
 
-    Now write your new plan below.
+    Now write your updated facts survey below, then your new plan.
 "managed_agent":
   "task": |-
     You're a helpful agent named '{{name}}'.

requirements.txt

@@ -1,3 +1 @@
-ddgs
-newspaper
 smolagents

tools/get_today_datetime.py

@@ -0,0 +1,22 @@
+from smolagents import Tool
+from typing import Any, Optional
+
+class SimpleTool(Tool):
+    name = "get_today_datetime"
+    description = "Get the current local date and time."
+    inputs = {}
+    output_type = "null"
+
+    def forward(self):
+        """
+        Get the current local date and time.
+
+        Returns:
+            str: Current date and time in the format 'YYYY-MM-DD HH:MM:SS'.
+
+        Example:
+            >>> forward()
+            '2025-08-29 15:42:18'
+        """
+        from datetime import datetime
+        return datetime.now().strftime("%Y-%m-%d %H:%M:%S")